Learning and Reasoning for Robot Dialog and Navigation Tasks
Learning and Reasoning for Robot Dialog and Navigation Tasks
复制标题
DOI：
10.26153/tsw/10939
复制
发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
Keting Lu;Shiqi Zhang;P. Stone;Xiaoping Chen
中科院分区：
文献类型：
--
作者：
Keting Lu;Shiqi Zhang;P. Stone;Xiaoping Chen
强化学习算法旨在从交互经验中学习，概率推理算法旨在利用概率上下文知识进行推理。在这项研究中，我们开发了机器人任务完成的算法，同时研究了强化学习和概率推理技术的互补优势。机器人从试错经验中学习，以增强其陈述性知识库，增强的知识可用于加快潜在不同任务的学习过程。我们已经实现并评估了使用移动机器人进行对话和导航任务的开发算法。从结果中，我们看到我们的机器人可以通过人类知识推理和从任务完成经验中学习来提高性能。更有趣的是，机器人能够从导航任务中学习，以改进其对话策略。
Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In this research, we develop algorithms for robot task completions, while looking into the complementary strengths of reinforcement learning and probabilistic reasoning techniques. The robots learn from trial-and-error experiences to augment their declarative knowledge base, and the augmented knowledge can be used for speeding up the learning process in potentially different tasks. We have implemented and evaluated the developed algorithms using mobile robots conducting dialog and navigation tasks. From the results, we see that our robot’s performance can be improved by both reasoning with human knowledge and learning from task-completion experience. More interestingly, the robot was able to learn from navigation tasks to improve its dialog strategies.